3D RoI Pooling: This approach extracts fixed size features from multi-scale proposals using 3D RoI pooling. Specifically, an input feature volume of size \(l \times h \times w\)  ( \(l\)  for temporal dimension, \(h\)  for height and \(w\)  for width dimensions) is divided into \(l_s \times h_s \times w_s\)  sub-volumes (where \(l_s, h_s,\)  and \(w_s\)  are fixed), and max pooling is performed inside each sub-volume. Therefore, proposals of various lengths generate output volume features of the same size, which is \(d \times l_s \times h_s \times w_s\) , where \(d\)  is the channel dimension. The idea of 3D RoI pooling for action detection is an extension of the 2D RoI pooling for object detection in Faster R-CNN [1]}. This idea was first introduced in R-C3D[2]} and used in other frameworks such as AGCN [3]} and AFNet [4]}. The limitation of this approach is that the multi-scale proposals at each location share the same receptive field, which may be too small or too large for some anchor scales.
